Clinicians and researchers have long sought to use brain imaging measures as an aid in clinical diagnosis. For example, in prior attempts to use magnetic resonance imaging (MRI) in the diagnosis of neuropsychiatric illnesses, conventional anatomical measures of putative pathological involvement, such as the overall volume of a brain region or combination of brain regions, have not proven particularly useful, possibly because brain regions that can be identified on MRI scans can be anatomically and functionally heterogeneous. For any given brain region, therefore, opposing measures of pathological involvement in its various subregions, such as volume loss in one subregion and compensatory hypertrophy or normal volumes in another, when combined into an overall volume, can be dilute and highly variable, producing substantial overlap between diagnostic groups in the distributions of overall volumes. The overlap in distributions, in turn, can yield poor sensitivity and specificity when trying to use those measures for clinical diagnosis. (See, e.g., Peterson B S. Form Determines Function: New Methods for Identifying the Neuroanatomical Loci of Circuit-Based Disturbances in Childhood Disorders. Journal of the American Academy of Child and Adolescent Psychiatry. 2010; 49(6):533-5). However, recent methods in image processing can permit measures of local variation in the morphological features of brain subregions that can be more anatomically and functionally homogeneous than conventional overall volumes (see, e.g., Peterson B S. Form Determines Function: New Methods for Identifying the Neuroanatomical Loci of Circuit-Based Disturbances in Childhood Disorders. Journal of the American Academy of Child and Adolescent Psychiatry. 2010; 49(6):533-5), and can be associated with various neuropsychiatric disorders. (See, e.g., Peterson B S, Choi H A, Hao X, Amat J, Zhu H, Whiteman R, et al. Morphology of the Amygdala and Hippocampus in Children and Adults with Tourette Syndrome. Archives General Psychiatry. 2007; Peterson B S, Warner V, Bansal R, Zhu H, Hao X, Liu J, et al. Cortical thinning in persons at increased familial risk for major depression. Proc Natl Acad Sci USA. 2009; 106:6273-8; Toga A W, Thompson P A. Mapping brain asymmetry. Nature Neuroscience. 2003; 4(1):37-48; Luders E, Narr K, Thompson P, Woods R, Rex D, Jancke L, et al. Mapping cortical gray matter in the young adult brain: Effects of gender. NeuroImage. 2005; 26(2):493-501; Davatzikos C, Shen D, Gur R C, Wu X, Liu D, Fan Y, et al. Whole-brain morphometric study of schizophrenia reveals a spatially complex set of focal abnormalities. Arch General Psychiatry. 2005; 62:1218-27; and Csernansky J G, Schindler M K, Splinter N R, Wang L, Gado M, Selemon L D, et al. Abnormalities of thalamic volume and shape in schizophrenia. Am J Psychiatry. 2004; 161:896-902).
Machine-based learning and pattern classification can include constructing procedures that can automatically learn decision rules for classification from experimental datasets and then apply the learned rules to classify individuals in other datasets. (See, e.g., Duda R O, Hart P E. Pattern Classification and Scene Analysis: John Wiley & Sons; 1973). These methods generally belong to either supervised or unsupervised classes of learning. For example, the pairs of data points {(xi,yi), i=1, . . . , n}, where xi∈Rm can be m-dimensional feature vectors and yi can be scalar-valued labels. The vectors xi can be brain measures and the labels yi can be clinical diagnoses. Supervised learning models can map between xi and yi using a parametric or nonparametric function ƒ(x), using a training sample to learn this function. The function can encode a decision rule or boundary that separates the feature vectors xi with the labels, yi. If the labels yi are missing, then methods for unsupervised learning (e.g., also termed data mining or clustering procedures) can be used to discover natural groupings within the data. The validity of these groupings is preferably established using data outside of the dataset that has been mined to generate the groupings.
Extant methods for machine-based classification of individual brains in imaging datasets can generally be characterized as supervised. (See, e.g., Lao Z, Shen D, Xue Z, Karacali B, Resnick S, Davatzikos C. Morphological classification of brains via high-dimensional shape transformations and machine learning methods. NeuroImage. 2004; 21:46-57; Klöppel S, Stonnington C M, Chu C, Draganski B, Scahill R I, Rohrer J D, et al. Automatic Classification of MR Scans in Alzheimer's Disease. Brain. 2008; 131(3):681-9; Duchesnay E, Cachia A, Roche A, Rivière D, Cointepas Y, Papadopoulos-Orfanos D, et al. Classification Based on Cortical Folding Patterns. IEEE Trans on Medical Imaging. 2007; 26(4):553-65; Davatzikos C, Fan Y, Wu X, Shen D, Resnick S M. Detection of prodromal Alzheimer's disease via pattern classification of MRI. Neurobiol Aging. 2008; 29(4):514-23; Liu Y, Teverovskiy L, Carmichael O, Kikinis R, Shenton M, Carter C S, et al., editors. Discriminative MR Image Feature Analysis for Automatic Schizophrenia and Alzheimer's Disease Classification 2004: Springer-Verlag GmbH, Saint-Malo, France; Teipel S J, Born C, Ewers M, Bokde A L, Reiser M F, Moller H J. Multivariate deformation-based analysis of brain atrophy to predict Alzheimer's disease in mild cognitive impairment. NeuroImage. 2007; 38(1):13-24; Fan Y, Shen D, Davatzikos C. Classification of structural images via high-dimensional image warping, robust feature extraction, and {SVM}. Med Image Comput Comput Assist Intery Int Conf. 2005; 8:1-8; Kawasaki Y, Suzuki M, Kherif F, Takahashi T, Zhou S Y, Nakamura K. Multivariate voxel-based morphometry successfully differentiates schizophrenia patients from healthy controls. NeuroImage. 2007; 34:235-42; Mourao-Miranda J, Bokde A L, Born C, Hampel H, Stetter M. Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data. NeuroImage. 2005; 28:980-95; Herholz K, Salmon E, Perani D, Baron J C, Holthoff V, Frolich L. Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET. NeuroImage. 2002; 17:302-16; Lerch J P, Pruessner J, Zijdenbos A P, Collins D L, Teipel S J, Hampel H, et al. Automated cortical thickness measurements from MRI can accurately separate Alzheimer's patients from normal elderly controls. Neurobiol Aging. 2006; 29(1):23-30; and Jack C R, Shiung M M, Gunter J L, O'Brien P C, Weigand S D, Knopman D S. Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD. Neurology. 2004; 62:591-600).
These methods typically use quantitative imaging data and known clinical diagnoses to learn optimal decision boundaries in the feature space that best separate individuals with specific illnesses. The quantitative features that enter the training dataset (e.g., the Jacobian matrix of the deformation field) have generally been extracted from images using voxel based morphometry (VBM) (see, e.g., Ashburner J, Friston K J. Voxel-based morphometry—the methods. NeuroImage. 2000; 11(6):805-21; and Lochhead R A, Parsey R V, Oquendo M A, Mann J J. Regional brain gray matter volume differences in patients with bipolar disorder as assessed by optimized voxel-based morphometry. Biol Psychiatry. 2004; 55(12):1154-62), a technique that can provide automated, quantitative, and fine-grained morphological information about the brain. These imaging measures, however, can have limitations when trying to classifying individuals accurately into diagnostic categories. Such imaging measures typically assume that a voxel in a template space (e.g., a brain from a healthy individual) represents corresponding anatomical region in the brains across individuals. This assumption is typically unlikely to be true, even when the brains of differing individuals have been spatially normalized to the template using high-dimensional deformations. This can be because the smoothness constraints employed when warping a brain into template space, together with the variability in anatomy across individual brains, can limit the ability of the normalization procedures to match precisely the corresponding anatomical regions across individuals.
Together, these limitations can introduce imprecision when identifying the point-to-point correspondences across brains and can allow measures from any point in the brain that is warped to template space to be influenced by the variable features of brain regions at a distance remote from that point. Although more recently developed algorithms for high-order nonlinear warpings (see, e.g., Ou Y, Davatzikos C. DRAMMS: deformable registration via attribute matching and mutual-saliency weighting. Inf Process Med Imaging. 2009; 21:50-62) can reduce these inaccuracies, brain features near but outside the regions of interest can likely still influence the smoothed deformation fields. An alternate method that can provide more accurate identification of point correspondences across brain surfaces, independent of morphological features at points remote from those surfaces, can be to delineate the surface of each brain region precisely and independently from other brain regions, and then to normalize each region independently to the corresponding region of the template brain.
Classification procedures that have been generated using supervised, machine-based learning procedure can identify diagnostic groupings based on imaging data sampled simultaneously and independently at voxels scattered across the brain. This sampling can identify voxels that contribute importantly to accurate classification when generating the diagnostic algorithm, but they can rarely classify accurately when applied to imaging data that are independent of the data that generated the algorithm. (See, e.g., Haubold A, Peterson B S, Bansal R. Progress in Using Brain Morphometry as a Diagnostic Tool for Psychiatric Disorders. The Journal of Child Psychology and Psychiatry. In Press).
Accordingly, there may be a need to address at least some of the above-described deficiencies.